Name
Abstract | ||
|---|---|---|
Image segmentation is one of the most difficult tasks in image processing and plays a critical role in the analysis of medical images used for diagnosis and treatment. With the decreased hardware costs and improvements in computing power of many-core architectures, there is an opportunity to both improve upon image segmentation algorithms and to make this technology more accessible. This paper describes our on-going research efforts to implement efficient image segmentation algorithms on graphical processing units (GPUs). A focused case study was performed with a suitable algorithm based on Cellular Automata, a parallel computational technique. Preliminary segmentation results are shown to validate our approach. Plans to improve the algorithm by making it more robust to noise and more efficient on GPU architectures are discussed. Our use of graph theoretic techniques and their implementation on GPUs will have broad application to other areas requiring computationally intensive calculations, as found in many problems involving modeling and simulation. |
Year | Venue | Keywords |
|---|---|---|
2013 | SummerSim | graphical processing unit,efficient image segmentation algorithm,cellular automata-based approach,image processing,image segmentation algorithm,gpu architecture,medical image,cellular automata,suitable algorithm,preliminary segmentation result,image segmentation |
Field | DocType | Citations |
Computational Technique,Graph,Cellular automaton,Modeling and simulation,Computer science,Segmentation,Simulation,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Irving Olmedo | 1 | 0 | 0.34 |
| Yessika Guerra Perez | 2 | 0 | 0.34 |
| James F Johnson | 3 | 26 | 4.53 |
| Lakshman Raut | 4 | 0 | 0.34 |
| David H. K. Hoe | 5 | 7 | 2.57 |